


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class 				A       (			_lowerCAmelCase				):
					'''simple docstring'''






					A__     =					None
					A__     =					None
					A__     =					None
					A__     =					None






class 				A       (			_lowerCAmelCase				):
					'''simple docstring'''






					def __init__(self       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any]=1 ,   _UpperCAmelCase       :		List[str]=0 ,   _UpperCAmelCase       :		Optional[Any]=2 ,   _UpperCAmelCase       :		List[Any]=512 ,   _UpperCAmelCase       :		Union[str, Any]="cls" ,   _UpperCAmelCase       :		Tuple=False ,   _UpperCAmelCase       :		int=True ,   **_UpperCAmelCase       :		str ,   )		->  Union[str, Any]:




							"""simple docstring"""


							super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,   bos_token_id=SCREAMING_SNAKE_CASE_ ,   eos_token_id=SCREAMING_SNAKE_CASE_ ,   **SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						project_dim
							lowercase__	     =						pooler_fn
							lowercase__	     =						learn_encoder
							lowercase__	     =						use_attention_mask






class 				A       (			_lowerCAmelCase				):
					'''simple docstring'''






					A__     =					[r'''pooler''', r'''logit_scale''']
					A__     =					[r'''position_ids''', r'''predictions.decoder.bias''']
					A__     =					'''roberta'''
					A__     =					RobertaSeriesConfig

					def __init__(self       :		Optional[int] ,   _UpperCAmelCase       :		Tuple						)		->  Optional[int]:




							"""simple docstring"""


							super().__init__(SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						XLMRobertaModel(SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						nn.Linear(config.hidden_size ,   config.project_dim						)
							lowercase__	     =						getattr(SCREAMING_SNAKE_CASE_ ,   """has_pre_transformation""" ,   SCREAMING_SNAKE_CASE_						)
							if self.has_pre_transformation:
									lowercase__	     =						nn.Linear(config.hidden_size ,   config.project_dim						)
									lowercase__	     =						nn.LayerNorm(config.hidden_size ,   eps=config.layer_norm_eps						)
							self.post_init()


					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		Optional[Any] = None ,   _UpperCAmelCase       :		Dict = None ,   _UpperCAmelCase       :		Tuple = None ,   _UpperCAmelCase       :		List[str] = None ,   _UpperCAmelCase       :		Tuple = None ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		Union[str, Any] = None ,   _UpperCAmelCase       :		Tuple = None ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		str = None ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						return_dict if return_dict is not None else self.config.use_return_dict

							lowercase__	     =						self.base_model(
							    input_ids=SCREAMING_SNAKE_CASE_ ,   attention_mask=SCREAMING_SNAKE_CASE_ ,   token_type_ids=SCREAMING_SNAKE_CASE_ ,   position_ids=SCREAMING_SNAKE_CASE_ ,   head_mask=SCREAMING_SNAKE_CASE_ ,   inputs_embeds=SCREAMING_SNAKE_CASE_ ,   encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,   encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,   output_attentions=SCREAMING_SNAKE_CASE_ ,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,   return_dict=SCREAMING_SNAKE_CASE_ ,   )

							if self.has_pre_transformation:
									lowercase__	     =						outputs["""hidden_states"""][-2]
									lowercase__	     =						self.pre_LN(SCREAMING_SNAKE_CASE_						)
									lowercase__	     =						self.transformation_pre(SCREAMING_SNAKE_CASE_						)

									return TransformationModelOutput(
									    projection_state=SCREAMING_SNAKE_CASE_ ,   last_hidden_state=outputs.last_hidden_state ,   hidden_states=outputs.hidden_states ,   attentions=outputs.attentions ,   )
							else:
									lowercase__	     =						self.transformation(outputs.last_hidden_state						)
									return TransformationModelOutput(
									    projection_state=SCREAMING_SNAKE_CASE_ ,   last_hidden_state=outputs.last_hidden_state ,   hidden_states=outputs.hidden_states ,   attentions=outputs.attentions ,   )






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






